Drug efficacy trials monitor the continued efficacy of front-line drugs against falciparum malaria. Overestimating efficacy results in a country retaining a failing drug as first-line treatment with associated increases in morbidity and mortality, while underestimating drug effectiveness leads to removal of an effective treatment with substantial practical and economic implications. Trials are challenging: they require long durations of follow-up to detect drug failures, and patients are frequently reinfected during that period. Molecular correction based on parasite genotypes distinguishes reinfections from drug failures to ensure the accuracy of failure rate estimates. Several molecular correction “algorithms” have been proposed, but which is most accurate and/or robust remains unknown. We used pharmacological modeling to simulate parasite dynamics and genetic signals that occur in patients enrolled in malaria drug clinical trials. We compared estimates of treatment failure obtained from a selection of proposed molecular correction algorithms against the known “true” failure rate in the model. Our findings are as follows. (i) Molecular correction is essential to avoid substantial overestimates of drug failure rates. (ii) The current WHO-recommended algorithm consistently underestimates the true failure rate. (iii) Newly proposed algorithms produce more accurate failure rate estimates; the most accurate algorithm depends on the choice of drug, trial follow-up length, and transmission intensity. (iv) Long durations of patient follow-up may be counterproductive; large numbers of new infections accumulate and may be misclassified, overestimating drug failure rate. (v) Our model was highly consistent with existing in vivo data. The current WHO-recommended method for molecular correction and analysis of clinical trials should be reevaluated and updated.

Antimalarial drugs have long half-lives, so clinical trials to monitor their efficacy require long periods of follow-up to capture drug failure that may become patent only weeks after treatment. Reinfections often occur during follow-up, so robust methods of distinguishing drug failures (recrudescence) from emerging new infections are needed to produce accurate failure rate estimates. Molecular correction aims to achieve this by comparing the genotype of a patient’s pretreatment (initial) blood sample with that of any infection that occurs during follow-up, with matching genotypes indicating drug failure. We use an in silico approach to show that the widely used match-counting method of molecular correction with microsatellite markers is likely to be highly unreliable and may lead to gross under- or overestimates of the true failure rates, depending on the choice of matching criterion. A Bayesian algorithm for molecular correction was previously developed and utilized for analysis of in vivo efficacy trials. We validated this algorithm using in silico data and showed it had high specificity and generated accurate failure rate estimates. This conclusion was robust for multiple drugs, different levels of drug failure rates, different levels of transmission intensity in the study sites, and microsatellite genetic diversity. The Bayesian algorithm was inherently unable to accurately identify low-density recrudescence that occurred in a small number of patients, but this did not appear to compromise its utility as a highly effective molecular correction method for analyzing microsatellite genotypes. Strong consideration should be given to using Bayesian methodology to obtain accurate failure rate estimates during routine monitoring trials of antimalarial efficacy that use microsatellite markers.

Introduction: Insecticide resistance threatens the control of important human vector-borne diseases such as malaria and dengue. The current de facto strategy is to target the insect vectors using a sequential deployment of insecticides i.e. use one insecticide (usually the cheapest available) until resistance has made it ineffective and then replace it with the next insecticide in the repertoire. Rotations of insecticides are often advocated as a potentially superior method of using the insecticide repertoire to delay the evolution of resistance. Testing this hypothesis in the field is logistically demanding and an in silico approach offers a much faster, flexible and transparent method of evaluating rotations. Methods: We develop an in silico approach to evaluate rotations using sequential deployment as the baseline. We explored a wide range of deployment scenarios, underlying genetics of resistance, and incorporated costs of resistance and gene flow to/from untreated refugia. Results: We found that, under most circumstances, resistance to all the insecticides in the repertoire were reached at very similar times for rotations and sequences. Any advantages of one strategy over the other tended to be small (typically <10%) and unpredictable. Conclusions: Operational factors, such as cost or supply-chain security, may therefore largely determine the optimal choice of insecticide deployment strategies.

Routine assessment of the efficacy of artemisinin-based combination therapies (ACTs) is critical for the early detection of antimalarial resistance. We evaluated the efficacy of ACTs recommended for treatment of uncomplicated malaria in five sites in Democratic Republic of the Congo (DRC): artemether-lumefantrine (AL), artesunate-amodiaquine (ASAQ), and dihydroartemisinin-piperaquine (DP). Children aged 6–59 months with confirmed Plasmodium falciparum malaria were treated with one of the three ACTs and monitored. The primary endpoints were uncorrected and polymerase chain reaction (PCR)-corrected 28-day (AL and ASAQ) or 42-day (DP) cumulative efficacy. Molecular markers of resistance were investigated. Across the sites, uncorrected efficacy estimates ranged from 63% to 88% for AL, 73% to 100% for ASAQ, and 56% to 91% for DP. PCR-corrected efficacy estimates ranged from 86% to 98% for AL, 91% to 100% for ASAQ, and 84% to 100% for DP. No pfk13 mutations previously found to be associated with ACT resistance were observed. Statistically significant associations were found between certain pfmdr1 and pfcrt genotypes and treatment outcome. There is evidence of efficacy below the 90% cutoff recommended by WHO to consider a change in first-line treatment recommendations of two ACTs in one site not far from a monitoring site in Angola that has shown similar reduced efficacy for AL. Confirmation of these findings in future therapeutic efficacy monitoring in DRC is warranted.

Background. Regulatory clinical trials are required to ensure the continued supply and deployment of effective antimalarial drugs. Patient follow-up in such trials typically lasts several weeks as the drugs have long half-lives and new infections often occur during this period. “Molecular correction” is therefore used to distinguish drug failures from new infections. The current WHO-recommend method for molecular correction uses length-polymorphic alleles at highly diverse loci but is inherently poor at detecting low density clones in polyclonal infections. This likely leads to substantial underestimates of failure rates, delaying the replacement of failing drugs with potentially lethal consequences. Deep sequenced amplicons (AmpSeq) substantially increase the detectability of low-density clones and may offer a new “gold standard” for molecular correction. Methods. Pharmacological simulation of clinical trials was used to evaluate the suitability of AmpSeq for molecular correction. We investigated the impact of factors such as the number of amplicon loci analysed, the informatics criteria used to distinguish genotyping ‘noise’ from real low density signals, the local epidemiology of malaria transmission, and the potential impact of genetic signals from gametocytes. Results. AmpSeq greatly improved molecular correction and provided accurate drug failure rate estimates. The use of 3 to 5 amplicons was sufficient, and simple, non-statistical, criteria could be used to classify recurrent infections as drug failures or new infections. Conclusions. These results suggest AmpSeq is strongly placed to become the new standard for molecular correction in regulatory trials, with its potential extension into routine surveillance once the requisite technical support becomes established.

Background Standard treatment for severe malaria is with artesunate; patient survival in the 24 hours immediately posttreatment is the key objective. Clinical trials use clearance rates of circulating parasites as their clinical outcome, but the pathology of severe malaria is attributed primarily to noncirculating, sequestered, parasites, so there is a disconnect between existing clinical metrics and objectives. Methods We extend existing pharmacokinetic/pharmacodynamic modeling methods to simulate the treatment of 10000 patients with severe malaria and track the pathology caused by sequestered parasites. Results Our model recovered the clinical outcomes of existing studies (based on circulating parasites) and showed a “simplified” artesunate regimen was noninferior to the existing World Health Organization regimen across the patient population but resulted in worse outcomes in a subgroup of patients with infections clustered in early stages of the parasite life cycle. This same group of patients were extremely vulnerable to resistance emerging in parasite early ring stages. Conclusions We quantify patient outcomes in a manner appropriate for severe malaria with a flexible framework that allows future researchers to implement their beliefs about underlying pathology. We highlight with some urgency the threat posed to treatment of severe malaria by artemisinin resistance in parasite early ring stages.

Immunoglobulin (Ig) is used to treat chronic inflammatory demyelinating polyradiculoneuropathy (CIDP) and multifocal motor neuropathy with conduction block (MMNCB). Regular infusions may be used for symptom control. Disease activity is monitored with clinical outcome measurements. We examined outcome measure variation during clinically stable periods in Ig‐treated CIDP and MMNCB patients. We explored utility of serial outcome measurement in long‐term outcome prediction. Retrospective longitudinal analysis of a single neuroscience centre's Ig‐treated CIDP and MMNCB patients, 2009‐2020, was performed. Mean and percentage change for grip strength, Rasch‐built overall disability scales (RODS) and MRC sum scores (MRC‐SS) during periods of clinical stability were compared to score‐specific minimal clinically important differences (MCID). Latent class mixed modelling (LCMM) was used to identify longitudinal trends and factors influencing long‐term outcome. We identified 85 CIDP and 23 MMNCB patients (1423 datapoints; 5635 treatment‐months). Group‐averaged outcome measures varied little over time. Intra‐individual variation exceeded MCID for RODS in 44.2% CIDP and 16.7% MMNCB datapoints, grip strength in 10.6% (CIDP) and 8.8%/27.2% (MMNCB right/left hand) and MRC‐SS in 43.5% (CIDP) and 20% (MMNCB). Multivariate LCMM identified subclinical trends towards improvement (32 patients) and deterioration (73 patients) in both cohorts. At baseline, CIDP ‘deteriorators’ were older than ‘improvers’ (66.2 vs 57 years, P = .025). No other individual factors predicted categorisation. The best model for ‘deteriorator’ identification was contiguous sub‐MCID decline in more than one outcome measure (CIDP: sensitivity 74%, specificity 59%; MMNCB: sensitivity 73%, specificity 88%). Outcome measure interpretation determines therapeutic decision‐making in Ig‐dependent neuropathy patients, but intra‐individual variation is common, often exceeding MCID. Here we show sub‐MCID contiguous changes in more than one outcome measurement are a better predictor of long‐term outcome.

Insecticides are widely used to control the insects that spread human infectious diseases, in particular falciparum malaria. This widespread use has driven insecticide resistance (IR) to high levels that may threaten the effectiveness of future control programmes. There is interest in identifying deployment methods that alleviate the pressures driving IR and we investigate three. Mixtures are, as already known, highly effective in slowing IR providing their effectiveness (ability to kill fully sensitive insects) remain close to 100%. Mixtures may be expensive and/or operationally difficult so two alternatives to mixtures were investigated. Panels, where different insecticides are physically closely adjacent, for examples, different panels on the same bednet; mosquitoes may therefore encounter both insecticides in the same foraging cycle. Micro-mosaics where different insecticides are deployed in slightly wider geographic proximity, for example in adjacent dwellings. The mosquitoes are unlikely to encountered both insecticides in the same foraging cycle but may encounter different insecticides in subsequent foraging. It is hoped that panels and/or micro-mosaics may, by allowing individual mosquitoes to potentially encounter both insecticides, be effective, lower-cost alternatives to mixtures. Our results suggest this is unlikely to be the case. When insecticides are fully effective then mixtures remain clearly the best strategy. As effectiveness falls then all three strategies are roughly equal. The operational decision of what deployment methods to use depends on how confident we are that insecticides will have high effectiveness that will be maintained in realistic field conditions post-deployment.